Thresholding species distribution models: Simple approaches for land-use planning in multifunctional landscapes

Species distribution models (SDMs) are often used to understand changes to species' distributions and their habitats under different land-use scenarios, enabling decision makers to prioritize areas for management efforts and balance environmental conservation with socio-economic demands on the landscape. However, the application of SDMs in land-use planning and Environmental Impact Assessments (EIAs) remains limited due to challenges in interpreting and communicating continuous predictions resulting from these SDMs. Using SDMs of 103 boreal bird species in Alberta, Canada, we transform species relative abundance predictions of SDMs into direct estimates of habitat area, a proxy for habitat suitability, using four simple and three complex thresholding methods. We found that thresholded models reflect losses in suitable habitat under industrial disturbance scenarios more realistically compared to continuous relative abundance models. Notably, simple thresholding methods, particularly the mean predicted relative abundance, performed similarly to complex thresholding methods in predicting suitable habitat areas, as indicated by model evaluations using the area under the curve. These findings suggest that using the mean as a binarization threshold can effectively bridge the gap between complex SDMs and their application in policy and planning, without sacrificing predictive accuracy.